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Nim, nim.py and games.py

Nim, nim.py and games.py. Homework 4 Problem 4. The History of Nim Games. Believed to have been created in China; unknown date of origin First actual recorded date- 15 th century Europe Originally known as Tsyanshidzi meaning “ picking stones game ”

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Nim, nim.py and games.py

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  1. Nim, nim.py and games.py Homework 4 Problem 4

  2. The History of Nim Games • Believed to have been created in China; unknown date of origin • First actual recorded date- 15th century Europe • Originally known as Tsyanshidzi meaning “picking stones game” • Presently comes from German word “nimm” meaning “take” Adapted from a presentation by Tim Larson and Danny Livarchik

  3. Rules of Nim • Impartial game of mathematical strategy • Strictly two players • Alternate turns removing any number of items from any ONE heap until no pieces remain • Must remove at least one item per turn • Last player to be able to remove a wins • Variations: • Initial number of heaps and items in each • Misere play: last player who can move loses • Limit on number of items that can be removed

  4. Demonstration Player 1 wins!

  5. Theoretical Approach • Theorem developed by Charles Bouton in 1901 • This states that in order to win, the goal is to reach a nim-sum of 0 after each turn until all turns are finished • Nim Sum: evaluated by taking the exclusive-or of the corresponding numbers when the numbers are given in binary form • Exclusive-or is used for adding two or more numbers in binary and it basically ignores all carries

  6. Tree for (2,1)

  7. Tree for (2,2)

  8. games.py • Peter Norvig’s python framework for multiple-player, turn taking games • Implements minimax and alphabeta • For a new game, subclass the Game class • Decide how to represent the “board” • Decide how to represent a move • A state is (minimally) a board and whose turn to move • Write methods to (1) initialize game instance, (2) generate legal moves from a state, (3) make a move in state, (4) recognize terminal states (win, lose or draw), (5) compute utility of a state for a player, (5) display a state

  9. Assumptions about states • games.py assumes that your representation of a state is a object with at least two attributes: to_move and board • The Struct class defined in utils.py can be used to create such instances • s = Struct(foo=‘a’, to_move = 1, board = [[1][2][3]]) • Access the attributes as s.to-move, etc.

  10. Caution • Python lists are mutable objects • If you use a list to represent a board and whant to generate a new board from it, you probably want to copy it fist new_board = board[:] new_board[3] = new_board[3] - 1

  11. Players The games.py framework defines several players • random_player: choses a random move from among legal moves • alphabeta_player: uses alpha_beta to choose best move, optional args specify cutoff depth (default is 8) and some other variations • human_player: asks user to enter move

  12. Variations defmake_alphabeta_player(N): """ returns a player function that uses alpha_beta search to depth N """ return lambda game, state: alphabeta_search(state, game, d=N) # add to the PLAYER dictionary player function named ab1,ab2,...ab20 # that use alpha_beta search with depth cutoffs between 1 and 20 for i in range(20): PLAYER['ab'+str(i)] = make_alphabeta_player(i)

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